「chi」の検索結果

End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.

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End-to-end flow, spectral, and mass cytometry (CyTOF) pipeline from raw FCS files to differentially abundant/expressed cell populations. Orchestrates the read -> compensate/unmix -> transform -> QC -> doublet-removal -> cluster-or-gate -> annotate -> diffcyt DA/DS chain with flowCore/CATALYST/diffcyt, branching on instrument type and on clustering-vs-gating. Use when processing a cytometry experiment end-to-end, deciding the pipeline path for an instrument, or wiring the flow-cytometry component skills into one analysis with valid sample-level statistics.

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PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.

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Assess drug-drug interactions — CYP metabolic interactions (substrate/inhibitor/inducer), transporter (P-gp, BCRP, OATP) effects, pharmacodynamic synergy/antagonism, clinical significance scoring, and management recommendations. Use for polypharmacy review, prescribing decision support, and safety analysis when adding or switching drugs.

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Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

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Organic chemistry reasoning guide for reaction product prediction, mechanism analysis (electrophilic/nucleophilic substitution, addition, elimination, pericyclic, radical), and spectroscopy interpretation (1H/13C NMR, IR, MS). Reasons from first principles (electron flow, kinetic vs thermodynamic) rather than pattern-matching named reactions. Use for organic synthesis problems and mechanism explanations.

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High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

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Dereplicate a putative natural product and assign its chemical taxonomy. Use to answer "is [compound] a known natural product", "what microbe/organism produces [compound]", "what chemical class is [compound]", "dereplicate this metabolite (by formula/exact mass/InChIKey/SMILES)", or "classify this molecule into ChemOnt". Searches NPAtlas for known microbial natural products (producing organism + literature reference), assigns the ChemOnt kingdom→superclass→class→subclass hierarchy via ClassyFire, resolves systematic IUPAC names to structure via OPSIN, and cross-references identity in PubChem. NOT for general drug/compound identity or ADMET (use tooluniverse-chemical-compound-retrieval / tooluniverse-small-molecule-discovery) and NOT for metabolomics pathway/enrichment analysis (use tooluniverse-metabolomics skills).

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Turns untargeted LC-MS/MS features (m/z, RT, MS/MS) into confidence-stratified metabolite annotations using spectral-library matching (matchms), in-silico tools (SIRIUS/CSI:FingerID, MetFrag) and molecular networking, and assigns a defensible MSI/Schymanski confidence level to each. Use when naming detected features, scoring MS/MS against a reference library, running SIRIUS, or deciding what confidence level an evidence set actually supports. For upstream feature extraction see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for downstream enrichment that must respect these levels see metabolomics/pathway-mapping; for lipid-specific structural annotation see metabolomics/lipidomics.

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Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.

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Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, MMTF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.

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Fetch external documents linked in SCF submissions — Google Docs, Google Drive PDFs, GitHub, Notion, IPFS. Use when a submission links to an architecture doc, whitepaper, or technical spec hosted externally. Handles URL transformation for Google services.

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Walk every branching path and boundary condition in content, report only unhandled edge cases. Orthogonal to adversarial review - method-driven not attitude-driven. Use when you need exhaustive edge-case analysis of code, specs, or diffs.

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Review Stellar Community Fund (SCF) Build Award applications. Use when evaluating SCF submissions, covering project fit assessment, technical architecture evaluation, team readiness, traction validation, budget analysis, and funding recommendations.

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Draft Stellar Community Fund Build Award applications interactively. Use when helping teams write their SCF submission — walks through project description, Stellar integration rationale, technical architecture, team section, deliverables, budget, and traction evidence. Produces a complete draft aligned with what funded projects look like.

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Verify implementation matches change artifacts. Use when the user wants to validate that implementation is complete, correct, and coherent before archiving.

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Sync delta specs from a change to main specs. Use when the user wants to update main specs with changes from a delta spec, without archiving the change.

日本語の概要は準備中です。原文の説明を表示しています。

React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.

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Rules for the VenTu data layer — scripts/, public/data/, scores, buoys (IH/WMO), Open-Meteo, IPMA, Ecowitt, news, Dawn Patrol. Use whenever touching data scripts, generated JSON, scoring, staleness display, or any data source.

日本語の概要は準備中です。原文の説明を表示しています。

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Progressively activates todo tests one at a time, updates implementation code until each passes (verified via Wallaby), checks off matching design doc progress, then moves to the next—following the design doc as the single source of truth.

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Select the existing owner test project and run checks matching the real failure mode.

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Prove repository ownership, module ownership, assembly boundaries, edit-marker rules, extension points, and test placement.

日本語の概要は準備中です。原文の説明を表示しています。

Review hot paths, allocations, iteration, caching, event frequency, and correctness-preserving optimization.

日本語の概要は準備中です。原文の説明を表示しています。